Binary segmentation of exon data.

Description

Compute the binary segmentation statistic, location and approximate p-value.

Usage

  exon.segment(gene, eloc, edat, ngrid=100, tol=1e-6)

Arguments

Details

The p-values are obtained by applying Siegmund's approximation for the maximal statistic from binary segmenting consecutive segments within a chromosome. These are one-sided test for an increase in expression.

Value

a matrix with three columns. The maximal statistic from binary segmentation, its location and the p-values for each gene.

Author(s)

Venkatraman E. Seshan

Examples

# test code on an easy data set
set.seed(25)
gene <- rep(c("A", "B"), c(30,20))
eloc <- c(1:30, 1:20)
edat <- matrix(rnorm(500), 50, 10)
# changes for gene1 in samples 3 & 7
edat[1:30, 3] <- edat[1:30, 3] + rep(0.9*0:1, c(17, 13))
edat[1:30, 7] <- edat[1:30, 7] + rep(1.1*0:1, c(21, 9))
# changes for gene2 in samples 4 & 7
edat[31:50, 4] <- edat[31:50, 4] + rep(1.1*0:1, c(8, 12))
edat[31:50, 7] <- edat[31:50, 7] + rep(1.2*0:1, c(13, 7))
exon.segment(gene, eloc, edat)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DNAcopy)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/DNAcopy/exon.segment.Rd_%03d_medium.png", width=480, height=480)
> ### Name: exon.segment
> ### Title: Binary segmentation of exon data.
> ### Aliases: exon.segment
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> 
> # test code on an easy data set
> set.seed(25)
> gene <- rep(c("A", "B"), c(30,20))
> eloc <- c(1:30, 1:20)
> edat <- matrix(rnorm(500), 50, 10)
> # changes for gene1 in samples 3 & 7
> edat[1:30, 3] <- edat[1:30, 3] + rep(0.9*0:1, c(17, 13))
> edat[1:30, 7] <- edat[1:30, 7] + rep(1.1*0:1, c(21, 9))
> # changes for gene2 in samples 4 & 7
> edat[31:50, 4] <- edat[31:50, 4] + rep(1.1*0:1, c(8, 12))
> edat[31:50, 7] <- edat[31:50, 7] + rep(1.2*0:1, c(13, 7))
> exon.segment(gene, eloc, edat)
$statistic
      [,1]     [,2]      [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
A 1.483811 0.975695 5.1779399 1.376714 2.756806 1.314609 3.512291 1.057993
B 1.019271 1.079689 0.8645837 1.740404 2.532542 2.221768 3.592733 3.234585
      [,9]    [,10]
A 1.572065 1.034291
B 1.322425 1.257206

$location
  [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
A   14   17   16   19    4    2   20   25   20    21
B    2   10    4    8    5    6   13   18    4     3

$p.value
       [,1]      [,2]         [,3]      [,4]       [,5]       [,6]        [,7]
A 0.4138045 0.6514291 2.233619e-06 0.4682891 0.03359905 0.49976199 0.003234689
B 0.5398790 0.5165852 5.909977e-01 0.2357449 0.04670747 0.09592211 0.001822153
         [,8]      [,9]     [,10]
A 0.619666867 0.3696976 0.6292630
B 0.006234493 0.4124617 0.4414358

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> dev.off()
null device 
          1 
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